Reducing flow delays in a data streaming application caused by lookup operations

ABSTRACT

Profiling data characterizing a data streaming application is used to predict data which will need to be retrieved by a processing element during execution of the data streaming application. Data is retrieved responsive to the prediction, in advance of actual demand by the processing element which requires it. Prediction may be based at least in part on upstream tuple contents, and could include other historical data retrieval patterns. In some embodiments, retrieval of predicted data may be delayed so that data is retrieved just in time.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of copending U.S. patent applicationSer. No. 15/798,773, filed Oct. 31, 2017, entitled “Reducing Flow Delaysin a Data Streaming Application Caused by Lookup Operations”, which is acontinuation of U.S. patent application Ser. No. 15/406,296, filed Jan.13, 2017, entitled “Reducing Flow Delays in a Data Streaming ApplicationCaused By Lookup Operations”, now issued as U.S. Pat. No. 10,417,239,both of which are herein incorporated by reference.

This application is also related to copending U.S. patent applicationSer. No. 16/514,728, filed Jul. 17, 2019, entitled “Reducing Flow Delaysin a Data Streaming Application Caused by Lookup Operations”, which isherein incorporated by reference.

FIELD

This disclosure generally relates to stream computing, and inparticular, to computing applications that receive streaming data andprocess the data as it is received.

BACKGROUND

In the latter half of the twentieth century, there began a phenomenonknown as the information revolution. While the information revolution isa historical development broader in scope than any one event or machine,no single device has come to represent the information revolution morethan the digital electronic computer. The development of computersystems has surely been a revolution. Each year, computer systems growfaster, store more data, and provide more applications to their users.

Modern computer systems may be used to support a variety ofapplications, but one common use is the maintenance of large relationaldatabases, from which information may be obtained. A large relationaldatabase is often accessible to multiple users via a network, any one ofwhom may query the database for information and/or update data in thedatabase.

Database systems are typically configured to separate the process ofstoring data from accessing, manipulating, or using data stored in adatabase. More specifically, database systems use a model in which datais first stored and indexed in a memory before subsequent querying andanalysis. In general, database systems may not be well suited forperforming real-time processing and analyzing streaming data. Inparticular, database systems may be unable to store, index, and analyzelarge amounts of streaming data efficiently or in real time.

Stream-based computing, also called data streaming, has been used tomore effectively handle large volumes of incoming data in real time. Ina data streaming application, data moves through a connected network of“processing elements” called a “graph”, each processing elementperforming some function or functions with respect to the data.

Stream-based computing works on a paradigm in which all the data is liveas it moves through the operator graph. In accordance with thisparadigm, each processing element in the graph has all the data neededto perform its function at hand, and can do so sufficiently rapidly tomaintain a high rate of data flow through the graph. However, aprocessing element sometimes needs to access data externally, i.e.,either in storage or a remote database, an event sometimes referred toas a lookup operation. When this happens, the processing element mustwait while the necessary data is retrieved. Such waits can substantiallydegrade the performance of the streaming application. Often, the waithas a ripple effect through the operator graph, causing other processingelements to wait unnecessarily for data and/or data to back up invarious buffers of the stream application.

A need exists for improved techniques for managing large data flows, andin particular, for improved data streaming techniques which manage datalookup operations.

SUMMARY

Profiling data collected from one or more previous time intervals duringa current and/or one or more previous execution instances of a datastreaming application is used to predict data which will need to beretrieved by a processing element in a current execution instance of thedata streaming application. Data is retrieved responsive to theprediction, in advance of actual demand by the processing element whichrequires it.

In one or more embodiments, prediction is based at least in part onupstream tuple contents. i.e., a particular set of values within a tupleencountered in the data streaming graph upstream of the subjectprocessing element may be used to predict a later need for certain databy the subject processing element, e.g., when the tuple reaches thesubject processing element. In one or more embodiments, prediction isbased at least in part on historical data retrieval patterns of the datastreaming application. Such historical patterns could include any or allof (a) time of day/week a data element is typically retrieved; (b) timeafter occurrence of a particular event; (c) existence of certainconditions; or (d) correlation with other data retrievals. In one ormore embodiments, prediction of required data may include a predictedtime the data is required, and retrieval of data which is predicted tobe required may be delayed so that data is retrieved just in time.

Prediction of data required need not be perfect. If data predicted to berequired is not in fact required, the data streaming application willcontinue to execute normally, and the only cost is the small overhead ofretrieving the unused data. If data is in fact required but notpredicted, it will be retrieved on demand of the processing element asin existing art. By predicting at least some data needed by one or moreprocessing elements in a data streaming application and retrieving datainto the processing element(s) in advance of demand by the processingelement(s) for the data, idling or waiting for data by the processingelement(s) is reduced, and efficiency of execution of the data streamingapplication is improved.

The details of the present invention, both as to its structure andoperation, can best be understood in reference to the accompanyingdrawings, in which like reference numerals refer to like parts, and inwhich:

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 illustrates a computing infrastructure configured to execute astream computing application according to various embodiments.

FIG. 2 is a high-level block diagram of the major hardware components ofa representative general purpose computer system which could be used toperform the role of any of several functional elements, according tovarious embodiments.

FIG. 3 is a conceptual illustration of certain software components inmemory of a compute node of FIG. 1, according to various embodiments.

FIG. 4 is a conceptual representation of a set of tuples in a streamingdata application buffer, according to various embodiments.

FIG. 5 is a conceptual illustration of certain software components inmemory of the management system of FIG. 1 according to variousembodiments.

FIG. 6 is a conceptual illustration of certain software components inmemory of the compiler system of FIG. 1 according to variousembodiments.

FIG. 7 illustrates an operator graph for a stream computing applicationaccording to various embodiments.

FIG. 8 is a conceptual illustration of the major software components inmemory of a database server computer system, according to variousembodiments.

FIG. 9 is a conceptual representation of an altered tuple of a datastreaming application altered for supporting profile analysis, accordingto various embodiments.

FIG. 10 is a flow diagram illustrating at a high level a process ofcollecting profile data for a data streaming application, according tovarious embodiments.

FIG. 11 is a conceptual illustration of the structure of an exemplaryset of lookup event summary records, according to various embodiments.

FIG. 12 is a flow diagram illustrating at a high level a process ofanalyzing profile data to produce a set of lookup event summary records,according to various embodiments.

FIG. 13 is a flow diagram illustrating at a high level a process ofgenerating or updating state data structures which govern lookupoperations during execution, according to various embodiments.

FIG. 14 is a flow diagram illustrating an execution process of anexemplary processing element of a data streaming application, accordingto various embodiments.

DETAILED DESCRIPTION

Streams Processing Overview

Stream-based computing and stream-based database computing are emergingas a developing technology for database systems. Products are availablewhich allow users to create applications that process and querystreaming data before it reaches a database file. With this emergingtechnology, users can specify processing logic to apply to inbound datarecords while they are “in flight,” with the results available in a veryshort amount of time, often in fractions of a second. Constructing anapplication using this type of processing has opened up a newprogramming paradigm that will allow for development of a broad varietyof innovative applications, systems, and processes, as well as presentnew challenges for application programmers and database developers.

In a stream computing application, stream operators are connected to oneanother such that data flows from one stream operator to the next (e.g.,over a TCP/IP socket). When a stream operator receives data, it mayperform operations, such as analysis logic, which may change the tupleby adding or subtracting attributes, or updating the values of existingattributes within the tuple. When the analysis logic is complete, a newtuple is then sent to the next stream operator. Scalability is achievedby distributing an application across nodes by creating executables(i.e., processing elements), as well as replicating processing elementson multiple nodes and load balancing among them. Stream operators in astream computing application can be fused together to form a processingelement that is executable. Doing so allows processing elements to sharea common process space, resulting in much faster communication betweenstream operators than is available using inter-process communicationtechniques (e.g., using a TCP/IP socket). Further, processing elementscan be inserted or removed dynamically from an operator graphrepresenting the flow of data through the stream computing application.A particular stream operator may or may not reside within the sameoperating system process as other stream operators. In addition, streamoperators in the same operator graph may be hosted on different nodes,e.g., on different compute nodes or on different cores of a computenode.

Data flows from one stream operator to another in the form of a “tuple.”A tuple is a sequence of one or more attributes associated with anentity. Attributes may be any of a variety of different types, e.g.,integer, float, Boolean, string, etc. The attributes may be ordered. Inaddition to attributes associated with an entity, a tuple may includemetadata, i.e., data about the tuple. A tuple may be extended by addingone or more additional attributes or metadata to it. As used herein,“stream” or “data stream” refers to a sequence of tuples. Generally, astream may be considered a pseudo-infinite sequence of tuples.

Tuples are received and output by stream operators and processingelements. An input tuple corresponding with a particular entity that isreceived by a stream operator or processing element, however, isgenerally not considered to be the same tuple that is output by thestream operator or processing element, even if the output tuplecorresponds with the same entity or data as the input tuple. An outputtuple need not be changed in some way from the input tuple.

Nonetheless, an output tuple may be changed in some way by a streamoperator or processing element. An attribute or metadata may be added,deleted, or modified. For example, a tuple will often have two or moreattributes. A stream operator or processing element may receive thetuple having multiple attributes and output a tuple corresponding withthe input tuple. The stream operator or processing element may onlychange one of the attributes so that all of the attributes of the outputtuple except one are the same as the attributes of the input tuple.

Generally, a particular tuple output by a stream operator or processingelement may not be considered to be the same tuple as a correspondinginput tuple even if the input tuple is not changed by the processingelement. However, to simplify the present description and the claims, anoutput tuple that has the same data attributes or is associated with thesame entity as a corresponding input tuple will be referred to herein asthe same tuple unless the context or an express statement indicatesotherwise.

Stream computing applications handle massive volumes of data that needto be processed efficiently and in real time. For example, a streamcomputing application may continuously ingest and analyze hundreds ofthousands of messages per second and up to petabytes of data per day.Accordingly, each stream operator in a stream computing application maybe required to process a received tuple within fractions of a second.Unless the stream operators are located in the same processing element,it is necessary to use an inter-process communication path each time atuple is sent from one stream operator to another. Inter-processcommunication paths can be a critical resource in a stream computingapplication. According to various embodiments, the available bandwidthon one or more inter-process communication paths may be conserved.Efficient use of inter-process communication bandwidth can speed upprocessing.

FIG. 1 illustrates one exemplary computing infrastructure 100 that maybe configured to execute a stream computing application, according tosome embodiments. The computing infrastructure 100 includes a managementsystem 105 and two or more compute nodes 110A-110D (herein genericallyreferred to as feature 110)—i.e., hosts—which are communicativelycoupled to each other using one or more communications networks 120. Thecommunications network 120 may include one or more servers, networks, ordatabases, and may use a particular communication protocol to transferdata among compute nodes 110. A compiler system 102 may becommunicatively coupled with the management system 105 and the computenodes 110 either directly or via the communications network 120.Additionally, a database system 115 containing a database 140 may becommunicatively coupled to network 120 for communication with managementsystem 105 and/or compute nodes 110.

The communications network 120 may include a variety of types ofphysical communication channels or “links.” The links may be wired,wireless, optical, or any other suitable media. In addition, thecommunications network 120 may include a variety of network hardware andsoftware for performing routing, switching, and other functions, such asrouters, switches, or bridges. The communications network 120 may bededicated for use by a stream computing application or shared with otherapplications and users. The communications network 120 may be any size.For example, the communications network 120 may include a single localarea network or a wide area network spanning a large geographical area,such as the Internet. The links may provide different levels ofbandwidth or capacity to transfer data at a particular rate. Thebandwidth that a particular link provides may vary depending on avariety of factors, including the type of communication media andwhether particular network hardware or software is functioning correctlyor at full capacity. In addition, the bandwidth that a particular linkprovides to a stream computing application may vary if the link isshared with other applications and users. The available bandwidth mayvary depending on the load placed on the link by the other applicationsand users. The bandwidth that a particular link provides may also varydepending on a temporal factor, such as time of day, day of week, day ofmonth, or season.

Computer System Hardware Components

FIG. 2 is a high-level block diagram of the major hardware components ofa representative general purpose computer system 200. In one or moreembodiments, compiler system 102, management system 105, compute nodes110, and database system 115 are each physically embodied as respectiveone or more general purpose computer systems, system 200 being arepresentation of any such general purpose computer system.

Computer system 200 includes one or more general-purpose programmableprocessors (CPU) 201 which execute instructions and process data frommain memory 202. Main memory 202 is preferably a volatile random accessmemory comprising at least one, and typically multiple, semiconductorintegrated circuit chip modules, using any of various memorytechnologies, in which data is loaded from storage or otherwise forprocessing by CPU(s) 201.

One or more communications buses 205 provide a data communication pathfor transferring data among CPU(s) 201, main memory 202 and variousinterface units 211, 212, 213, which may also be known as I/O processors(IOPs) or I/O adapters (IOAs). The interface units support communicationwith a variety of storage, I/O devices, and/or networks. For example,storage interface unit(s) 211 supports the attachment of one or morestorage devices 221-223 providing non-volatile storage of data which canbe loaded into memory as required. Such storage devices may includewell-known rotating magnetic hard disk drive storage devices, solidstate devices (SSD), removable memory cards, optical storage, flashmemory, and so forth, and could further include network attached storage(NAS), devices attached via a storage area network (SAN), and/or arraysof disk drives and/or other storage devices configured to appear as asingle large storage device to a host. Storage may further include cloudstorage devices accessible via one or more networks. I/O deviceinterface unit(s) 212 may support the attachment of any of various othertypes of I/O devices, such as user terminals, displays, keyboards orother input devices, printers, and so forth, it being understood thatother or additional types of I/O devices could be used. Networkinterface adapter(s) 213 may support connections to one or more externalnetworks for communication with one or more other digital devices, andspecifically to network 120 for communication with devices representedin FIG. 1. Network adapter(s) 213 could support redundant connections toa single network, or could be coupled to separate networks, which may ormay not be in communication with each other. Such external networkspreferably include the Internet, and may include one or moreintermediate networks, such as local area networks, through whichcommunication with the Internet is effected.

It should be understood that FIG. 2 is intended to depict therepresentative major components of general purpose computer system 200at a high level, that individual components may have greater complexitythan represented in FIG. 2, that components other than or in addition tothose shown in FIG. 2 may be present, that the number, type andconfiguration of such components may vary, and that a complex computersystem will typically have more components than represented in FIG. 2.Several particular examples of such additional complexity or additionalvariations are disclosed herein, it being understood that these are byway of example only and are not necessarily the only such variations.

Although only a single CPU 201 is shown for illustrative purposes inFIG. 2, computer system 200 may contain multiple CPUs, as is known inthe art. Although main memory 202 is shown in FIG. 2 as a singlemonolithic entity, memory 202 may in fact be distributed and/orhierarchical, as is known in the art. For example, memory may exist inmultiple levels of caches, and these caches may be further divided byfunction, so that one cache holds instructions while another holdsnon-instruction data which is used by the processor or processors.Memory may further be distributed and associated with different CPUs orsets of CPUs, as is known in any of various so-called non-uniform memoryaccess (NUMA) computer architectures Although communications buses 205are shown in FIG. 2 as a single entity, in fact communications amongvarious system components is typically accomplished through a complexhierarchy of buses, interfaces, and so forth, in which higher-speedpaths are used for communications between CPU(s) 201 and memory 202, andlower speed paths are used for communications with I/O interface units211-213. Buses 205 may be arranged in any of various forms, such aspoint-to-point links in hierarchical, star or web configurations,multiple hierarchical buses, parallel and redundant paths, etc. Forexample, as is known in a NUMA architecture, communications paths arearranged on a nodal basis. Buses may use, e.g., an industry standard PCIbus, or any other appropriate bus technology. While multiple I/Ointerface units are shown which separate buses 205 from variouscommunications paths running to the various I/O devices, it wouldalternatively be possible to connect some or all of the I/O devicesdirectly to one or more system buses. Although FIG. 1 represents network120 as a single entity, in one or more embodiments a separate network orstorage bus may be present for communicating with one or more sharedstorage servers, and such communication may be driven by a dedicated oneor more storage interface units 211 separate from general purposenetwork adapters 213.

Computer system 200 depicted in FIG. 2 may include multiple attachedterminals, such as might be typical of a multi-user “mainframe” computersystem. Where computer system 200 is used exclusively as a compute node110 or other server for performing work on behalf of remote clients, itmay contain only a limited number of terminals, or even a singleterminal, e.g., for use as a maintenance interface by a systemadministrator or the like, or in some cases no terminal at all,administrative functions being performed remotely. Furthermore, whilecertain functions are described herein for illustrative purposes asembodied in a single computer system, some or all of these functionscould alternatively be implemented using a distributed network ofcomputer systems in communication with one another, in which differentfunctions or steps described herein are performed on different computersystems.

Although compute nodes 110, management system 105, compiler system 102,and database system 115 are represented in FIGS. 1-2 as independentsystems, any or all of these entities may be implemented ascorresponding logical partitions of one or more logically partitionedcomputer systems. For example, any of CPUs 201 may in fact be acorresponding portion of a processing resource capacity of a largerlogically partitioned computer system which is allocated to thecorresponding logical partition; and any memory 202 may in fact be acorresponding portion of a memory capacity of a larger logicallypartitioned computer system which is allocated to the correspondinglogical partition.

While various system components have been described and shown at a highlevel, it should be understood that a typical computer system containsmany other components not shown, which are not essential to anunderstanding of the present invention.

Stream Computing Components

FIG. 3 is a conceptual illustration showing in greater detail certainsoftware components in memory 300 of a compute node 110, which may bethe same as one of the compute nodes 110A-110D of FIG. 1, according tovarious embodiments. As shown in FIG. 3, a representative compute nodememory includes an operating system kernel 301, one or more processingelements 311, and a buffer 313.

Operating system kernel 301 is executable code and state data providingvarious low-level software functions, such as device interfaces,management of memory pages, management and dispatching of multipletasks, common services for application programs, etc. as is well knownin the art. In particular, OS kernel 301 preferably includes one or morenetwork adapter drivers 302 for handling communications with one or morenetworks, including network 120, via network interface(s) 213.

The one or more processing elements 311 each comprise code and statedata for performing respective functions as part of a data streamcomputing application. A stream computing application may include one ormore stream operators 312 that may be compiled into a “processingelement” container 311. The memory 300 may include two or moreprocessing elements 311, each processing element having one or morestream operators 312. Each stream operator 312 may include a portion ofcode that processes tuples flowing into a processing element and outputstuples to other stream operators 312 in the same processing element, inother processing elements, or in both the same and other processingelements in a stream computing application. Processing elements 311 maypass tuples to other processing elements that are on the same computenode 110 or on other compute nodes that are accessible viacommunications network 120. For example, a processing element 311 oncompute node 110A may output tuples to a processing element 311 oncompute node 110B. In one embodiment, a processing element 311 isassigned to be executed by only one CPU, although in other embodimentsthe stream operators 312 of a processing element 311 may includemultiple threads which may be executed on different respective CPUs.

Buffer 313 is a portion of memory 300 for holding data being streamed aspart of the stream computing application, and in particular, for holdingdata tuples. Buffer 313 may be a single entity, but in one or moreembodiments, multiple buffers exist including a local buffer 314 alsoknown as a window, one or more TCPIP buffers 315 for passing messagesamong various entities of the data streaming applications, and one ormore thread ports 316 for queuing data to be processed be respective oneor more threads.

FIG. 4 is a conceptual representation of a set of tuples in a streamingdata application buffer 313, according to various embodiments. Any ofbuffers 314-316 may hold one or more tuples. As illustrated in FIG. 4, aset of tuples contains one or more tuples 401, each tuple logicallyorganized as multiple fields or attributes 402-404. A set of tuples maybe conceptually represented as a table, in which each tuple 401corresponds to a respective row of the table, and each attribute orfield of a tuple corresponds to a respective column of the table.Although conceptually represented as a table, the actual structure of aset of tuples in memory may vary, and may be different in each of thedifferent buffers 314-316; the set may occupy non-contiguous memoryaddress regions, tuples may vary in size; some attributes might bepresent in only a subset of the tuples, and so forth. Although invarious embodiments the data streaming application produces tuples whichare added to a table of a database, the structure and attributes oftuples 401 within the data streaming application are not necessarilyidentical to those of tuples in a table of a relational database. Thetuples 401 stored in buffer 313 may be all of a single type (i.e., allhave the same attributes and structure), or may be tuples of differenttypes. In one or more embodiments, tuples may be grouped separately inseparate sets or in different buffers depending on current status of atuple within the operator graph. For example, one set of tuples maycomprise tuples 401 awaiting processing by a particular processingelement 311 within the compute node while another set comprises tuples401 which have already been processed by the particular processingelement

In one or more embodiments, compute node memory 300 may further includetrace data 318 tracing operation of the data streaming application, andparticularly tracing the progression of tuples through the operatorgraph. This trace data may be used to build operator graph profile datafor use in projecting downstream data lookup operations within theoperator graph.

In one or more embodiments, state data in processing elements 311further includes a lookup mask 321 and one or more trigger parameterdatasets 322. Lookup mask 321 is a bit mask or similar structureindicating, for each of one or more processing elements, operators, orother code portions which may trigger anticipatory lookup operation inresponse to detecting an anteceding lookup predictive event, whetheranticipatory lookup is enabled. Trigger parameter datasets 322 comprisesone or more datasets, each corresponding to a respective processingelement, operator, or other location, which contains parametersgoverning triggering the lookup event and the consequent lookup eventitself. The usage of the lookup mask 321 and trigger parameter datasets322 during execution of the data streaming application is described ingreater detail herein.

FIG. 5 is a conceptual illustration showing in greater detail certainsoftware components in memory 500 of the management system 105 of FIG. 1according to various embodiments. As shown in FIG. 5, a representativemanagement system memory includes an operating system kernel 501, astream manager 134, an operator graph 136, a profile data analyzer 523,and operator graph profile data 525.

Operating system kernel 501 is executable code and state data providingvarious low-level software functions, such as device interfaces,management of memory pages, management and dispatching of multipletasks, common services for application programs, etc. as is well knownin the art. In particular, OS kernel 501 preferably includes one or morenetwork adapter drivers 502 for handling communications with one or morenetworks, including network 120, via network interface(s) 213.

Stream manager 134 manages the operation of the data streamingapplication, and in particular, maintains operator graph 132. Operatorgraph 132 is a data structure defining how tuples are routed toprocessing elements 311 for processing.

In one or more embodiments, state data in stream manager furtherincludes a lookup mask 521 and one or more trigger parameter datasets522. This state data is similar to local copy of lookup mask 321 andlocal copies of trigger parameter datasets 322 in memory 300 of acompute node 110, but stream manager would have global state data forthe entire data streaming application, whereas the local copies of thisdata in memory 300 of a compute node would generally hold only statedata applicable to the processing elements within that compute node. Theusage of the lookup mask 521 and trigger parameter datasets 522 duringexecution of the data streaming application is described in greaterdetail herein.

Profile data analyzer 523 is executable code and state data whichcollects trace data from the various compute nodes and analyzes thatdata to construct and maintain operator graph profile data 525. Operatorgraph profile data 525 includes lookup event summary records 526 andtrace data 527. Trace data 527 is a collection of all or selectiveportions of trace data 318 from the various compute nodes, and is usedto by profile data analyzer to generate lookup event summary records526. Lookup event summary records are a representation of historicallookup event patterns. For example, lookup event summary records maysummarize, for each of various antecedent lookup predictive events, arespective predicted subsequently occurring lookup event, and mayoptionally include a probability or probabilities associated with therespective antecedent event and/or measure of time delay between theantecedent event and the lookup event. The antecedent event may be arespective tuple of a particular type, which may have particularattribute values, occurring at a particular processing elements.Exemplary lookup event summary data 526 is illustrated in FIG. 11 anddescribed in greater detail herein.

FIG. 6 is a conceptual illustration showing in greater detail certainsoftware components in memory 600 of the compiler system 102 of FIG. 1according to various embodiments. As shown in FIG. 6, a representativecompiler system memory includes an operating system kernel 601, acompiler 136, and compiler input and output in the form of sourcemodules 611, intermediate code modules 612, and object code modules 613.

Operating system kernel 601 is executable code and state data providingvarious low-level software functions, such as device interfaces,management of memory pages, management and dispatching of multipletasks, common services for application programs, etc. as is well knownin the art. In particular, OS kernel 601 preferably includes one or morenetwork adapter drivers 602 for handling communications with one or morenetworks, including network 120, via network interface(s) 213.

Compiler 136 is executable code and data structures which compilesmodules, which include source code or statements 611, into the objectcode 613, which includes machine instructions that execute on aprocessor. In one embodiment, the compiler 136 may translate the modulesinto an intermediate form 612 before translating the intermediate forminto object code. The compiler 136 may output a set of deployableartifacts that may include a set of processing elements and anapplication description language file (ADL file), which is aconfiguration file that describes the stream computing application. Insome embodiments, the compiler 136 may be a just-in-time compiler thatexecutes as part of an interpreter. In other embodiments, the compiler136 may be an optimizing compiler. In various embodiments, the compiler136 may perform peephole optimizations, local optimizations, loopoptimizations, inter-procedural or whole-program optimizations, machinecode optimizations, or any other optimizations that reduce the amount oftime required to execute the object code, to reduce the amount of memoryrequired to execute the object code, or both. The output of the compiler136 may be represented by an operator graph, e.g., the operator graph132.

The compiler 136 may also provide the application administrator with theability to optimize performance through profile-driven fusionoptimization. Fusing operators may improve performance by reducing thenumber of calls to a transport. While fusing stream operators mayprovide faster communication between operators than is available usinginter-process communication techniques, any decision to fuse operatorsrequires balancing the benefits of distributing processing acrossmultiple compute nodes with the benefit of faster inter-operatorcommunications. The compiler 136 may automate the fusion process todetermine how to best fuse the operators to be hosted by one or moreprocessing elements, while respecting user-specified constraints. Thismay be a two-step process, including compiling the application in aprofiling mode and running the application, then re-compiling and usingthe optimizer during this subsequent compilation. The end result may,however, be a compiler-supplied deployable application with an optimizedapplication configuration.

Compiler system memory 600 further includes common run-time code 614.Common run-time code can be any of source code, intermediate code, orobject code. Common run-time code 614 is common code which is includedin the code of each processing element 311 to perform functions commonto all or many processing elements. Common run-time code may include,for example, functions for passing messages among the various processingelements, accessing buffer 313, reporting errors or other status, and soforth. In one or more embodiments, common run-time code includes traceinstructions 615 for collecting trace data 318 tracing operation of thedata streaming application, and anticipatory lookup instructions 616 forinitiating an anticipatory lookup operation responsive to a lookuppredictive event. Trace data 318 collected by executing traceinstructions 615 may be used for building operator graph profile data525. Trace instructions 615 and/or anticipatory lookup instructions 616may be optionally included instructions, i.e., instructions which thecompiler 136 optionally includes in the code of a processing elementdepending on the settings or directions given to the compiler at time ofcompilation.

FIG. 7 illustrates an exemplary operator graph 700 for a streamcomputing application beginning from one or more sources 702 through toone or more sinks 704, 706, according to some embodiments. This flowfrom source to sink may also be generally referred to herein as anexecution path. In addition, a flow from one processing element toanother may be referred to as an execution path in various contexts.Although FIG. 7 is abstracted to show connected processing elementsPE1-PE10, the operator graph 700 may include data flows between streamoperators 312 (FIG. 3) within the same or different processing elements.Typically, processing elements, such as processing element 311 (FIG. 3),receive tuples from the stream as well as output tuples into the stream(except for a sink—where the stream terminates, or a source—where thestream begins). While the operator graph 700 includes a relatively smallnumber of components, an operator graph may be much more complex and mayinclude many individual operator graphs that may be statically ordynamically linked together.

The example operator graph shown in FIG. 7 includes ten processingelements (labeled as PE1-PE10) running on the compute nodes 110A-110D. Aprocessing element may include one or more stream operators fusedtogether to form an independently running process with its own processID (PID) and memory space. In cases where two (or more) processingelements are running independently, inter-process communication mayoccur using a “transport,” e.g., a network socket, a TCP/IP socket, orshared memory. Inter-process communication paths used for inter-processcommunications can be a critical resource in a stream computingapplication. However, when stream operators are fused together, thefused stream operators can use more rapid communication techniques forpassing tuples among stream operators in each processing element.

The operator graph 700 begins at a source 702 and ends at a sink 704,706. Compute node 110A includes the processing elements PE1, PE2, andPE3. Source 702 flows into the processing element PE1, which in turnoutputs tuples that are received by PE2 and PE3. For example, PE1 maysplit data attributes received in a tuple and pass some data attributesin a new tuple to PE2, while passing other data attributes in anothernew tuple to PE3. As a second example, PE1 may pass some received tuplesto PE2 while passing other tuples to PE3. Tuples that flow to PE2 areprocessed by the stream operators contained in PE2, and the resultingtuples are then output to PE4 on compute node 110B. Likewise, the tuplesoutput by PE4 flow to operator sink PE6 704. Similarly, tuples flowingfrom PE3 to PE5 also reach the operators in sink PE6 704. Thus, inaddition to being a sink for this example operator graph, PE6 could beconfigured to perform a join operation, combining tuples received fromPE4 and PE5. This example operator graph also shows tuples flowing fromPE3 to PE7 on compute node 110C, which itself shows tuples flowing toPE8 and looping back to PE7. Tuples output from PE8 flow to PE9 oncompute node 110D, which in turn outputs tuples to be processed byoperators in a sink processing element, for example PE10 706. Typically,the sinks 704, 706 output data (e.g. tuples) externally of the datastreaming application (e.g., to a database, storage file, or otherdestination); however, it is possible for any of the processing elementsto output data externally as well.

Processing elements 311 (FIG. 3) may be configured to receive or outputtuples in various formats, e.g., the processing elements or streamoperators could exchange data marked up as XML documents. Furthermore,each stream operator 312 within a processing element 311 may beconfigured to carry out any form of data processing functions onreceived tuples, including, for example, writing to database tables orperforming other database operations such as data joins, splits, reads,etc., as well as performing other data analytic functions or operations.

The stream manager 134 may be configured to monitor a stream computingapplication running on compute nodes, e.g., compute nodes 110A-110D, aswell as to change the deployment of an operator graph, e.g., operatorgraph 132. The stream manager 134 may move processing elements from onecompute node 110 to another, for example, to manage the processing loadsof the compute nodes 110A-110D in the computing infrastructure 100.Further, stream manager 134 may control the stream computing applicationby inserting, removing, fusing, un-fusing, or otherwise modifying theprocessing elements and stream operators (or what tuples flow to theprocessing elements) running on the compute nodes 110A-110D.

Because a processing element may be a collection of fused streamoperators, it is equally correct to describe the operator graph as oneor more execution paths between specific stream operators, which mayinclude execution paths to different stream operators within the sameprocessing element. FIG. 7 illustrates execution paths betweenprocessing elements for the sake of clarity.

Database Components

In accordance with one or more embodiments, tuples output by operatorgraph 700, whether from one of sinks 704, 706, or from some otherprocessing element, are entered into one or more tables of a structuredrelational database 140. FIG. 8 is a conceptual illustration of themajor software components in memory 800 of a database server computersystem 115 of FIG. 1 for accessing a structured relational database 140,according to various embodiments. As shown in FIG. 8, a database servercomputer system memory contains an operating system kernel 801 andstructured database 140 including a database manager 811, one or moredatabase tables 821-823, and one or more metadata structures 824-832.

Operating system kernel 801 is executable code and state data providingvarious low-level software functions, such as device interfaces,management of memory pages, management and dispatching of multipletasks, common services for application programs, etc. as is well knownin the art. In particular, OS kernel 801 preferably includes one or morenetwork adapter drivers 802 for handling communications with one or morenetworks, including network 120, via network interface(s) 213.

Database tables and metadata 820 include one or more tables 821-823 (ofwhich three are shown for illustrative purposes in FIG. 8, it beingunderstood that the number may vary). As is known in the database art, adatabase table is a data structure logically in the form of a tablehaving multiple records (also called entries or tuples), each recordhaving at least one, and usually multiple, fields (also calledattributes). The “rows” of the table correspond to the records, and the“columns” correspond to the fields. Although tables 821-823 are datastructures which are logically equivalent to tables, they may bearranged in any suitable structure known in the database art. Databasetables 821-823 might contain almost any type of data which is useful tousers of a computer system.

Associated with the database tables are one or more auxiliary datastructures 824-832, also sometimes referred to as metadata (of whichnine are represented in FIG. 8, it being understood that the number andtype of such structures may vary). Auxiliary data structurescharacterize the structure of the database and data therein, and areuseful in various tasks involved in database management, particularly inexecuting queries against the database. Examples of auxiliary datastructures include database indexes 824-827, histograms 828-829, andmaterialized query tables (MQT) 830-831). Auxiliary data structures mayfurther include a query cache 832 in which data regarding previouslyexecuted queries (the query itself, query execution plan or executionstrategy, run-time statistics from execution, etc.) is stored. Althougha particular number and type of auxiliary database structures isillustrated in FIG. 8, it will be understood that the number and type ofsuch structures may vary, that not all illustrated structures may bepresent, and/or that additional structures not shown may be present.

Database manager 811 comprises executable computer programming codewhich executes on CPU(s) 201 of database server system 115 to providebasic functions for the management of database 140. Database manager 811may theoretically support an arbitrary number of database tables, whichmay or may not have related information, although only three tables areshown in FIG. 8. Database manager 811 preferably contains administrativemaintenance functions 812 which automatically perform certain functionsto manage the database and/or allow authorized users to perform basicadministrative operations with respect to the database, such as definingand editing database table definitions, creating, editing and removingrecords in the database, viewing records in the database, definingdatabase auxiliary data structures such as indexes and materializedquery tables, views, and so forth. Administrative functions may furtherinclude logging of database transactions, recovery of data, and soforth. Certain of these functions may be available only to systemadministrators and the like, while others are available to clients.

Database manager 811 preferably further includes a query engine 813 forexecuting queries against data in database tables 821-823 and a queryoptimizer 814 for generating optimized query execution plans for use byquery engine 813 in executing queries. Database manager 811 furtherpreferably includes an external interface 815 having one or moreapplication programming interfaces (APIs) by which external applicationscan access data in database 140 either by invoking query engine 813 orthrough other means. Database manager 811 may further contain any ofvarious more advanced database functions, as are known in the art.Database manager could be a generic database management system, such asone implementing a structured query language (SQL) query protocol, butit might alternatively query and structure data according to some otherprotocol and/or might be a custom designed database management system.Although database manager 811 is shown and described herein as an entityseparate from operating system kernel 801, it will be understood that insome computer architectures various database management functions areintegrated with the operating system.

Although one database 140 having three database tables 821-823 and nineauxiliary structures 824-832 are shown in FIG. 8, the number of suchentities may vary, and could be much larger. A computer system or agroup of computer systems may contain multiple databases, each databasemay contain multiple tables, and each database may have associated withit multiple indexes, MQTs, histograms, views, volatility records, and/orother auxiliary data structures not illustrated. Alternatively, someentities represented in FIG. 8 might not be present in all databases.Additionally, database 140 may be logically part of a larger distributeddatabase which is stored on multiple computer systems. Although databasemanager 811 is represented in FIG. 8 as part of database 140, thedatabase manager, being executable code, is sometimes considered anentity separate from the “database”, i.e., the data 820.

In addition to operating system 801 and database 140, memory of databasesystem 800 may include all or selective portions of one or more userapplications 804-805. User applications 804-805 are applications whichexecute on CPU(s) 201, and may access data in database 140 to performtasks on behalf of one or more users. Such user applications mayinclude, e.g., sales transactions, inventory management, personnelrecords, accounting, code development and compilation, mail,calendaring, or any of thousands of user applications, and may beweb-based (i.e., present web pages to a remote client for rendering inthe client's browser) or provide some other form of user interface. Someof these applications may access database data in a read-only manner,while others have the ability to update data. There may be manydifferent types of read or write database access tasks, each accessingdifferent data or requesting different operations on the data. Forexample, one task may access data from a specific, known record, andoptionally update it, while another task may invoke a query, in whichall records in the database are matched to some specified searchcriteria, data from the matched records being returned, and optionallyupdated. Furthermore, data may be read from or written to databasetables 811-813 directly, or may require manipulation or combination withother data supplied by a user, obtained from another database, or someother source. Applications 804-805 typically utilize function calls todatabase manager 811 through external APIs 815 to access data in thedatabase, and in particular, to execute queries against data in thedatabase, although in some systems it may be possible to independentlyaccess data in the database directly from the application. Although twoapplications 804-805 are shown for illustrative purposes in FIG. 8, thenumber of such applications may vary.

Various software entities are represented conceptually in FIGS. 3-8 asbeing contained in respective memories of any of the various systems ordevices described herein. However, as is well known, the memory of acomputer or other digital device is typically insufficient to hold allsoftware entities and other data simultaneously, and selective portionsof software entities or other data are typically loaded into memory fromstorage as required. Furthermore, various software entities arerepresented in FIGS. 3-8 as being separate entities or contained withinother entities. However, it will be understood that this representationis for illustrative purposes only, and that particular modules or dataentities could be separate entities, or part of a common module orpackage of modules. Furthermore, although a certain number and type ofsoftware entities are shown in the conceptual representations of FIGS.3-8, it will be understood that the actual number of such entities mayvary, and in particular, that in a complex data streaming and/ordatabase environment, the number and complexity of such entities istypically much larger. Additionally, although certain softwarecomponents are depicted in within respective single systems forcompleteness of the representation, it is not necessarily true that allprograms, functions and data will be present in a single system, and maybe present in another partition on the same computer system or in adifferent computer system. For example, user applications 804-805 whichcall APIs to access the database may be on a separate system fromcertain maintenance functions such as defining the database, adding ordeleting metadata structures, and so forth. Finally, it will beunderstood that the conceptual representations of FIGS. 3-8 are notmeant to imply any particular memory organizational model, and that acomputer system hosting a data streaming application or a database mightemploy a single address space virtual memory, or might employ multiplevirtual address spaces which overlap.

Collection of Data Streaming Profile Data

In accordance with one or more embodiments, profile data is collectedwhich characterizes the operation of the data streaming application.This profile data is then used to correlate instances of external dataretrieval (lookup events) by a processing element in the operator graphwith previously occurring events/conditions (antecedent lookuppredictive events). In particular, in accordance with one or moreembodiments, these antecedent events include particular data within atuple upstream of the processing element which causes the external dataretrieval. The antecedent events may also include particular values ofexternal state variables, such as a time of day/day of week and soforth. These antecedent events can then used to predict that processingelement will subsequently need to retrieve data. I.e., during subsequentexecution of the data streaming application, occurrence of theantecedent events associated with later need for particular externaldata will cause the external data to be retrieved in advance of actualdemand for it by the processing element which requires it.

In one or more embodiments, profile data is obtained by tracing theexecution of one or more execution instances of the data streamingapplication, although other or additional forms of profile data might beused, such as input and output data or data obtained from analysis ofthe source code. Tracing is a well-known technique whereby theoccurrence of pre-defined traceable events during execution of acomputer program causes the computer to save certain state data showingthe state of the computer at the time the traceable event occurred. Itis typically used during computer program code development, to debugerrors, determine frequently used code paths, identify performancebottlenecks, and so forth.

Tracing may be accomplished by “instrumenting” the code to be traced,i.e., placing trace instructions (“instrumentation”) at various codelocation which, when encountered during execution of the computerprogram, cause the desired state data to be saved. A trace instructioncould cause data to be saves unconditionally (every time the instructionis encountered), or conditionally based on some state value(s). Theexact mechanism whereby the state data is saved may vary. The tracinginstrumentation could be in-line instructions in the code, or a call toa separate routine, or an instruction which triggers an interrupt.

In one or more embodiments, the trace instructions 615 (instrumentation)are contained in at least one version of the common run-time code 614used by computer 136 to generate the data streaming program. Therecould, in fact, be multiple versions of the common run-time code,including one without any instrumentation. There could also be multipledifferent instrumented versions for collecting different types ofprofile data. Instrumentation in the common run-time code simplifies theprocess of developing a data streaming application by avoiding the needfor developers of each different data streaming application to createtheir own instrumentation, and standardizes the collection and analysisof profile data.

The common run-time code 614 typically contains routines in whichtraceable events occur. Specifically, in one or more embodiments, commonrun-time code 614 will include routines for allocating a new tuple inthe data streaming application, for sending a tuple from one processingelement to a next processing element, for accessing data outside thedata stream (which may include lookup events), and for outputting atuple to the database. Additional routines which may be of interest intracing tuples in accordance with one or more embodiments may includeroutines for copying or duplicating a tuple, for deleting a tuple, forchanging the definition of a tuple (its fields, field lengths, etc.) andso forth. In one or more embodiments, any or all of these events mightbe traceable events which cause the collection of trace data, andappropriate instrumentation is placed in the corresponding routineswhich perform the operation.

In one or more embodiments, the instrumented version(s) of commonrun-time code 614 alter the structure of the tuples used in the datastreaming application by adding additional data useful in traceanalysis. FIG. 9 is a conceptual representation of an altered tuple 901of a data streaming application, altered for supporting profile analysisof trace data, according to various embodiments. Referring to FIG. 9,tuple 901 includes a header 902 containing a tuple type 903, a uniquetuple identifier 904, and a variable number of parent pairs 905, eachparent pair comprising a respective parent tuple type 906 and parenttuple identifier 907. The header may contain other data. The tuplefurther contains a body portion 908 having a variable number of userdata fields 909-911 as defined by the data streaming application, ofwhich three are illustrated in FIG. 9, it being understood that thenumber of such user data fields may vary. The tuple type 903 is the nameof a set of tuples having a common defined structure, correspondingroughly to a table name of a database table containing multiple tuples(also called records or rows). The tuple identifier 904 and parent pairs905 are additional fields which are added by the instrumented version ofthe common run-time code 614. These fields are used internally by thedata streaming application for trace analysis and/or other purposes, andneed not be visible to the user of the application.

In the instrumented version of the common run-time code, any routinewhich creates a new tuple automatically allocates the above describedfields and assigns a unique tuple identifier 904, similar to a uniqueserial number, to the newly created tuple. If the newly created tuple iscreated from or copied from an existing tuple (parent tuple), the tupletype and unique tuple identifier of the parent tuple are copied into aparent pair 905 of the new tuple as the parent tuple type 906 and parenttuple identifier 907, respectively. Since there could be a chain ofmultiple parents, all parent pairs 905 in the immediate parent are alsocopied into respective parent pairs 905 of the newly created tuple.

FIG. 10 is a flow diagram illustrating at a high level a process ofcollecting profile data for a data streaming application, according tovarious embodiments. Referring to FIG. 10, the instrumented code iscompiled by compiler 136 (block 1001). Compilation represented at block1001 could be either static or dynamic compilation. If staticallycompiled, the user would direct compilation with instrumentation atcompile time, by specifying use of an appropriate instrumented versionof the common run-time code, or if supported, by a special compilerdirective or option to use the instrumented version. If dynamicallycompiled at run time, the user invoking execution of the data streamingapplication specifies the code files (e.g., source or intermediate code)including any instrumented version of the common run-time code.Responsive to the appropriate directive, compiler 136 compiles the datastreaming application (either statically or dynamically, as the case maybe) to incorporate the tracing instructions.

The data streaming application is invoked for execution with tracingenabled, and any optional tracing parameters are specified (block 1002).Although block 1002 is represented in FIG. 10 as following block 1001,is will be understood that in certain dynamic compilation environments,compilation may occur after the data streaming application is invokedfor execution.

In one or more embodiments, the instrumentation instructions, being inthe common run-time code, are not specific to any particular datastreaming application and therefore not specific to any particular typeof tuple or set of tuple types. For example, a common run-time routinewhich sends a tuple from one processing element to a next processingelement could include a tracing instructions which trigger whenever atuple (of any type) is sent. If the user wishes to trace a particulartype of tuple or set of tuple types, the user specifies the tuple(s) tobe traced as an optional tracing parameter when the data streamingapplication is invoked. When the trace instructions are triggered, thecode determines whether the tuple being operated on by the correspondingcommon run-time routine is of the type which should be traced, and savestrace data accordingly. Additional run-time tracing options arepossible. For example, it may be desirable to trace only some if thepossible traceable events or paths through the operator graph. Whilegeneric trace instructions may exist in the common run-time code makingit possible to trace all paths through the operator graph, the user mayspecify particular paths to be traced or otherwise limit the events tobe traced.

Accordingly, when the data streaming application is invoked forexecution at block 1002, the user may specify any tracing parameters.The user may have the option to disable tracing entirely for performancereasons. To collect trace data for use in analyzing the executionprofile of the data streaming application and generating operator graphprofile data 525 including profile lookup event data 526, tracing ispreferably enabled and trace data for one or more tuple types ofinterest is saved whenever a lookup event occurs, a tuple of thecorresponding type is created, is sent from one processing element toanother, or is output to the database. Additional events may optionallybe traced. At least initially, it would typically be expected that alllookup events would be traced along with the creation or transmission ofcorresponding tuples. But after a data streaming application has beenpreviously profiled, a user may wish to trace particular lookup eventswhich are known to be of interest.

Stream manager 134 responds by initiating execution in the variouscompute nodes 110 and initializing any environmental parameters,including environmental parameters governing tracing (block 1003). Forexample, a trace enable flag may be set, and bit masks or other datastructures may be initialized to control tracing for the desired eventsto be traced, trace data to be collected, and so forth.

The data streaming application executes concurrently in each of thecompute nodes 110 and in the management system 105, represented in FIG.10 as blocks 1004A-D. Actions within each node or management system areillustrated only in block 1004A for clarity of representation, it beingunderstood that these are similar in blocks 1004B-D. Within each node(or management system), the data streaming application code executes,possibly in multiple concurrent threads (represented in simplified formas block 1005), until a trace instruction is encountered. The traceinstruction causes a check whether tracing is enabled (block 1006). Ifnot, the ‘N’ branch is taken from block 1006, and execution resumes. Iftracing is enabled, trace code determines whether the event and thecurrent state data match the tracing parameters which were specifiedwhen execution was invoked (block 1007). For example, if particularevents such as a lookup operation, tuple creation, and/or tupletransmission from one processing element to another, the trace codeverifies that the trace instruction causing temporary halt in executioncame from one of these events; if tracing of a particular tuple type wasspecified, the trace code verifies that the tuple associated with thetrace event is of the specified type; and so forth. If the event/statedata do not match the specified tracing parameters, the ‘N’ branch istaken from block 1007, and execution resumes; otherwise, the ‘Y’ branchis taken, and the trace code determines the extent of trace data to besaved (block 1008). Almost any data could be saved in a trace, but inone or more embodiments, the saved trace data includes a copy of thetuple associated with the traceable event and the location in theoperator graph at which the tuple was at the time of the traceableevent. This data is then saved in the local trace data 318, or, if thetrace instructions are executing in the management node, in managementnode trace data 527 (block 1009).

At some point, an exit condition is encountered during execution,causing execution of the program to end, as indicated by the flow lineto the END block. Such an exit condition could be, e.g., completion ofprocession all data, an interrupt, an error condition, or other exitcondition.

Profile trace data could be collected by tracing during one or multipleexecution instances of the data streaming application and/or multipletime intervals during a single execution, and might be refined orperiodically updated over time as more is learned about the behavior ofthe data streaming application or as changes to the application code orthe data upon which it typically operates cause changes to theapplication's behavior.

Generation of Lookup Event Summary Data

In accordance with one or more embodiments, the collected profile datais analyzed using profile data analyzer 523 in management system 105 toproduce a set of lookup event summary records 526. The lookup eventsummary records correlate antecedent lookup predictive events such asspecific tuple types and/or tuple attribute values and or other statevariables occurring at specific locations within the operator graph withsubsequently occurring lookup events, and may further specify somemeasure of the probability or likelihood that the subsequent lookupevent will actually occur and/or the time delay between the occurrenceof the antecedent lookup predictive event and the subsequent lookupevent.

As used herein, a “lookup event” is a retrieval of data outside thebuffers and caches of the data streaming application, i.e. a retrievalfrom storage or a remote device, as a result of a current need for thedata by an executing processing element of the data streamingapplication. This is similar to a page fault during execution of aconventional computer program, but is broader in the sense that it couldinclude data accessed over a network. The lookup event necessarily takesconsiderable time to retrieve the required data, which may affect theperformance of the data streaming application. Because the streamed datais held in the various buffers, the streamed data tuples themselves areautomatically available to the processing element and are not retrievedin lookup events. The lookup event may be necessary to retrieve otherdata which is somehow necessary to process the tuples in the processingelements. The lookup event is therefore associated with a tuple,specifically, the tuple being processed by the processing element whichtriggered the lookup event, although the associated tuple itself doesnot need to be retrieved in the lookup event.

FIG. 11 is a conceptual illustration of the structure of an exemplaryset of lookup event summary records 526, according to variousembodiments. These records correlate antecedent lookup predictive eventsin the data streaming application with subsequently occurring lookupoperations, and may be used, among other things, to trigger theinitiation of a lookup operation responsive to an antecedent event whichpredicts it, before an actual need for the looked-up data is encounteredin the executing data stream.

Referring to FIG. 11, the lookup event summary data 526 containsmultiple records 1101, each record corresponding to a single pair of anantecedent lookup predictive event and a resultant lookup operation. Theantecedent event is expressed as a tuple of a specified type, andoptionally having one or more specified attribute values, occurring at aspecified location in the operator graph, and optionally under one ormore specified external state variable values. Each record 1101 in thelookup event summary data contains a trigger location field 1102specifying the location in the operator graph at which the antecedentevent (i.e., the presence of a particular tuple) occurs; an internaltuple type field 1103 specifying the type of tuple which is temporarilyheld at the corresponding location within the operator graph toconstitute the antecedent event; an internal tuple count field 1104specifying the number of internal tuples of the type specified in typefield 1103 which were found in the trace data at the trigger locationspecified in trigger location 1102; and a number of lookup events field1105 specifying the number of lookup events in the group of lookupevents to which the lookup event summary record corresponds.

In one or more embodiments, each record 1101 further contains a lookuplocation field 1106 specifying the location in the operator graph atwhich the lookup operation is to occur, i.e. to which looked up data isto be loaded; and a lookup event descriptor 1108 defining the resultantlookup event. The lookup event descriptor may contain any data needed todefine the particular lookup operation which results from thecorresponding antecedent event, and may include, but is not necessarilylimited to: a network path, device path, device identifier or similaridentifying a storage device, network location, or other entity fromwhich the looked up data is to be retrieved; a filename, address, and/orother data specifying a location of the data to be looked up within theentity from which the looked up data is to be retrieved; a number ofpages or other measure of amount to data to be retrieved; a loaddestination specifying an address or other designator of a location towhich the looked up data is to be loaded; and any other data which maybe necessary to define the lookup operation.

Each record 1101 may optionally further contain delay parameters 1107which collectively specify certain time delays regarding the lookupevent, and may be used where appropriate to delay initiation of the datalookup operation during execution following detection of the antecedentlookup predictive event. Delay parameters 1107 may include one or morevalues specifying a time elapsed between the occurrence of theantecedent event and the need by the data streaming application for thedata which is the subject of the specified data lookup operation, andone or more values specifying a time elapsed from the initiation of adata lookup operation until the retrieved data is available to the datastreaming application. Alternatively, the delay parameters could be acombined value in which both of these quantities are combined to expressa delay time between detection of a lookup predictive event andinitiation of a data lookup operation. Any of these quantities withinthe delay parameters 1107 could be a single value or multiple values,e.g., a mean time interval and a standard deviation from that mean, andif a single value, could represent an average time elapsed, a minimumtime elapsed, a minimum time elapsed of some predetermined portion ofthe lookup operations, or some other measure of time.

In one or more embodiments, the record further contains one or more keyfield identifiers 1109 (of which one is illustrated in FIG. 11), eachspecifying a key field within the internal tuple type specified in field1103, and a variable number of key field specifics 1110 corresponding toeach key field identifier, each specific specifying a correspondingminimum value 1111, a corresponding maximum value 1112, and acorresponding probability value 1113. The probability value 1113expresses a probability that the subsequently occurring lookup eventwill occur given that the antecedent lookup predictive event occurs,where the antecedent event is a tuple of type specified in internaltuple type field 1103, at graph location 1102, having a keyfield valuein keyfield 1109 within the range specified by minimum value 1111 andmaximum value 1112. The probability value may be expressed as a floatingpoint value between 0 and 1. Alternatively, the probability value may bestored as a pair of values, the probability value being derived as thequotient of the pair of values. For example, the pair of values may betwo integers which represent a count of a number of subsequent lookupevents and a count of a number of tuples of type IT (and optionallyhaving particular parameters) found at the subject graph location.

In one or more further embodiments, the record 1101 may further includeone or more external state variable identifiers 1114 (of which one isillustrated in FIG. 11), each specifying an external state variable(i.e. a variable external to the tuple), and a variable number ofexternal state variable specifics 1115 corresponding to each externalstate variable, each such specific specifying a corresponding minimumvalue 1116, a corresponding maximum value 1117, and a correspondingprobability value 1118. The probability value 1118 is similar toprobability value 1113, and expresses a probability that thesubsequently occurring lookup event will occur given that the antecedentlookup predictive event occurs, i.e., that a tuple of type specified ininternal tuple type field 1103 is encountered, at graph location 1102,and the specified external state variable 1114 has a value within therange specified by minimum value 1116 and maximum value 1117. Theprobability value may be expressed in any manner stated above withrespect to probability value 1113.

In one or more alternate embodiments, the key field 1109 and key fieldspecifics 1110 and/or the external state variable identifier 1114 andexternal state variable specifics 1115 may be optional or not used. Asingle probability value may be specified for the antecedent internaltuple type and graph location, i.e., a single probability valueregardless of the values of any data within the corresponding tuple. Inone or more further alternative embodiments, probability values are notused, and it is assumed that if the antecedent event occurs, thesubsequent lookup event will also be necessary.

The data collected by tracing may be analyzed in any of various ways toproduce lookup event summary records 526. Conceptually, the analysisamounts to determining, for each subsequently occurring lookup event,for each location the tuple causing the lookup or a parent tuple thereofpassed through in the operator graph, and for each internal tuple (orparent tuple) at that location, the number of such tuples at thelocation (antecedent events) and the number of subsequently occurringlookup events. Additionally, if one or more key fields and/or externalstate variables are identified, these numbers are broken down by rangeof values in the corresponding key field or external state variable.Additionally, once the antecedent events and subsequent lookup eventsare identified, one or more measures of delay (average, minimum, etc.)between the occurrence of the antecedent event and lookup event can bedetermined.

FIG. 12 is a flow diagram illustrating at a high level a process ofanalyzing profile data to produce a set of lookup event summary records526, according to various embodiments. This analysis is performed by orunder the control of profile data analyzer 523 in management system 105.

Referring to FIG. 12, trace data collected in the various nodes andstored in respective local node trace data caches 318 of compute nodes110 is transmitted to the management system 105 for analysis (block1201). Collection of trace data in the management system is shown as asingle block for simplicity of representation. It would in fact bepossible to transmit all trace data to the management system at thebeginning of analysis, as represented in FIG. 12. Alternatively, profiledata analyzer 523 in management system 105 may request trace data inincrements from the various compute nodes 110 as the analysis isperformed. This latter approach would off-load some of the screeningwork to the compute nodes. For example, the management system maydetermine that only specific lookup events occurring at specificlocations are of interest, and according request data pertaining only tothose lookup events, thereby reducing consumption of network bandwidthduring analysis, and reducing the burden on the management system ofscanning a great deal of trace data which is ultimately not used.

The lookup events in the trace data are identified and categorized intogroups according to the originating cause in the data streamingapplication and source of the looked up data (block 1202). In one ormore embodiments, this means that lookup events generated by executionof the same processing element, on behalf of the same tuple type, andaccessing the same external data source, are grouped together. The same“external data source” does not necessarily mean exactly the same data,for exactly the same data is unlikely to be looked up repeatedly, as itwill in the normal course be maintained in some sort of cache. Anexternal data source may, for example, be a very large data entity suchas an external database, in which one or more attributes of the tuplebeing processed by the respective processing element are used todetermine which data from the external data source is accessed. Amongother things, the external data source could be database 140 or anyparticular table 821-823 thereof, or could be some other database ortable thereof, or could be an array or other data structure inmanagement system 105, any of compute nodes 110, or compiler system 102,or any other data accessible locally or over network 120. One or moreattributes of the tuple being processed may be used to determine anaddress, a key value, an array index, or some other value which is usedto identify the specific data within a larger external data source(database, array or other data structure) which is retrieved by thelookup operation.

In one or more optional embodiments, the groups of lookup events areprioritized and selected for analysis (block 1203). The lookup eventgroups may be prioritized according to some appropriate measure ofadverse effect on performance of the data streaming application which isattributable to the corresponding category of lookup operations. Forexample, the groups may be prioritized according to total number oflookup operations in each group, or total cumulative time required toperform the lookup operations in each group, or some other measure ofeffect on performance. One or more groups are selected for analysis. Inone embodiment, all groups having a performance impact exceeding somepre-determined threshold are selected. In another embodiment, groupshaving a performance effect in excess of some deviation from an averageperformance effect are selected. In another embodiment, only the grouphaving the largest effect on performance is selected.

Analysis of groups of lookup events according to some measure of adverseeffect on performance is intended to avoid analysis of and subsequentcorrective actions for categories of lookup events having little or noeffect on performance. For example, some types of lookup events mayoccur only rarely, as when some error condition is encountered. Attemptsto predict such rarely occurring lookup events may be subject toconsiderable inaccuracy, and the overhead of doing so may exceed anyperformance benefit. However, in one or more embodiments, optional block1203 is not performed, and all groups of lookup events are analyzed.

A next group from among those prioritized for analysis is then selectedas the current group to be analyzed (block 1204). For each location inthe operator graph, the trace data is analyzed to produce a set of oneor more lookup event summary records 1101 corresponding to the group oflookup events selected for analysis. This is represented in FIG. 12 asblocks 1205-10. A “location” can be any subset of the operator graph inthe data streaming application in which tuples might be temporarily heldand at which they are traced. In one or more embodiments, the“locations” are processing elements in the operator graph, and are sodescribed herein, it being understood that the granularity of locationscould be compute nodes, operators, or some other entity.

Since each group corresponds to a particular category of lookup event,the corresponding lookup events necessarily occur at a particularprocessing element in the data streaming application on behalf of aparticular tuple type. The profile data analyzer accesses the operatorgraph data to determine a predecessor processing element in the operatorgraph from which the tuple type which caused the lookup event (or aparent of that tuple type) came (block 1205). I.e., it traverses theoperator graph backwards. If such a predecessor exists (the ‘Y’ branchfrom block 1206), it is selected as the current processing element foranalysis. For simplicity of description, it is assumed herein that, foreach processing element at which such a tuple type is present, there isonly one such predecessor processing element, although it is in factpossible that multiple predecessor processing elements exist, in whichcase each such processing element is analyzed in turn.

The profile data analyzer scans the trace to identify all occurrences ofthe corresponding tuple type (or parent thereof) in the currentprocessing element (block 1207); these form a current set of internaltuples. A corresponding lookup event summary record 1101 is generatedfor the current processing element and lookup event group (block 1208).The trigger location 1102 of the lookup event summary record is thecurrent processing element; the internal tuple type 1103 is thecorresponding tuple type (or parent thereof) in the current processingelement, the internal tuple count 1104 is the number of tuples found inthe trace; the number of lookup events 1105 is the number of lookupoperations in the current group of lookup operations; the lookuplocation 1106 is the processing element at which the lookups in thecurrent group occur, and the lookup event descriptor 1108 is adescriptor containing the identifying parameters of the lookups of thecurrent group. The ratio of the number of internal tuples (field 1104)to the number of lookup events (field 1105) yields an approximateprobability that, upon encountering a tuple of the specified type at thetrigger location during execution, a subsequent lookup event of thecategory of lookup events which form the current group of lookup eventswill be necessary.

In one or more embodiments, the trace data is analyzed to determine oneor more delay parameters 1107, which are added to the lookup eventsummary record 1101 (block 1209). The delay parameters may be used todelay, in appropriate circumstances, initiation of a lookup event afterdetection of the antecedent lookup predictive event during execution.Ideally, the delay after detection of the antecedent lookup predictiveevent would be just sufficient to cause the looked up data to becomeavailable immediately before it is needed by the lookup location'sprocessing element. This can be determined as a function of the timeinterval between occurrence of the antecedent lookup event (i.e., thetuple traced in the applicable trigger location) and the beginning ofthe lookup operation (indicating need for the data), less the timerequired to perform the lookup operation. In many cases, the latter timemay exceed the former time interval, indicating that the data lookupoperation should commence as soon as the antecedent lookup predictiveevent is detected. Since these time intervals will not necessarily beuniform, the trace data may be analyzed to determine some measure oftypical or average behavior, such as a mean time interval and a standarddeviation thereof, and both the interval between antecedent event andlookup event, and the time required for data lookup, may be separatelymeasured. It may alternatively be possible to obtain measurements ofaverage lookup operation time from some other source, such asperformance monitoring statistics.

In one or more embodiments, the trace data is analyzed to identify anycorrelations between particular attribute values in the tuple and/orexternal state variable values and a subsequent lookup event for thatsame tuple (block 1210). If a correlation is found between an attributevalue in the tuple and the subsequent lookup event, a keyfield 1109 andone or more associated key field specifics 1110 may be appended to thelookup event summary record generated at block 1208. Similarly, if acorrelation is found between an external state variable value and thesubsequent lookup event, an external state variable identifier 1114 andone or more associated external state variable specifics may be appendedto the lookup event summary record.

Specifically, it is desirable to know whether, for any values of any keyattribute field in the internal tuples of the corresponding internaltuple type or for any external state variable values, the probability ofa subsequent lookup operation in the current group of lookup operationsbeing performed is substantially different than for the set of internaltuples of the same tuple type as a whole (i.e, the ratio of number oftuples field 1104 to number of lookup events field 1105). The keyfield(s) and/or external state variable(s) could be specified by someexternal command to the stream manager, or could be determined by theprofile data analyzer 523 by analyzing the trace data. Specifically, anyof various analytical techniques or tools could be used for findingcorrelations in data.

If such a key field or external state variable and corresponding valueranges are identified, the key field/external state variable is saved askey field 1109 or external state variable ID 1114 in the lookup eventsummary record 1101, and each value or range of values of interest, andtheir corresponding lookup event probabilities, are saved as arespective key field specific 1110 or external state variable specific1115 having a respective minimum value 1111 or 1116, a respectivemaximum value 1112 or 1117, and a respective lookup event probability1113 or 1118. The probability reflects the probability that, given atuple occurring in the trigger location and having a key field value inthe corresponding range (or under conditions when an external statevariable is in the corresponding range), a lookup event of the currentgroup of lookup events will subsequently be required for the same tuple.

In one embodiment, a key field 1109 or external state variable ID 1114is saved in the lookup event summary record 1101 (along withcorresponding key field specific 1110 or external state variablespecific 1115) only for those values for which the corresponding lookupevent probability is significantly greater than the lookup eventprobability for the set of internal tuples of the same tuple type as awhole. In an alternative embodiment, a key field 1109 or external statevariable ID 1114 is saved in the lookup event summary record 1101 (alongwith corresponding key field specific 1110 or external state variablespecific 1115) for those values for which the corresponding lookup eventprobability is significantly different (whether greater or less than)the lookup event probability for the set of internal tuples of the sametuple type as a whole. In another alternative embodiment, if the lookupevent probability for the set of internal tuples of the same tuple typeas a whole is sufficiently large, indicating that the corresponding datashould always be looked up when the tuple is encountered at the triggerlocation, block 1210 may be skipped, and no key fields 1109 or externalstate variable IDs 1114 (along with corresponding key field specific1110 or external state variable specific 1115) are appended to thelookup event summary record 1101

After delay parameters have been computer (block 1209) and trace dataanalyzed for correlations between specific attribute/external variablevalues and subsequent lookups (block 1210), the analyzer returns toblock 1205 to determine another predecessor location in the graph.

If, at block 1206, no predecessor processing element exists, theoperator graph has been traversed all the way back to creation of thecorresponding tuple, and the ‘N’ branch is taken from block 1206,indicating that the corresponding group of lookup events has beenanalyzed. In this case, if any more selected groups of lookup eventsremain to be analyzed, the ‘Y’ branch is taken from block 1211 and anext group is selected at block 1204. When all groups have been thusanalyzed, the ‘N’ branch is taken from block 1211, and analysis ofprofile data is complete.

It will be appreciated that in the above description and theillustration of FIG. 12, various actions are shown and described asbeing performed sequentially for ease of understanding. However, forgreater system efficiency, it may be possible to perform many of theseactions concurrently by combining multiple scans of trace data into asingle scan. It will further be understood that the order of certainactions could be changed without affecting the result of generatingprofile summary records. Finally, it will be appreciated that manyvariations in the form of profile data used to analyze lookup events andantecedent lookup predictive events are possible, and that the lookupevent summary records described herein represent only some of thepossible forms of profile data which may be used. Profile records mayhave other or additional fields; may be based on data other than or inaddition to trace data; may characterize data streaming applicationbehavior in a different way; and so forth.

Early Lookup Operations During Execution

In accordance with one or more embodiments, a respective trigger isinserted at one or more trigger locations each identified by acorresponding lookup event summary record. Encountering the triggerduring execution either is a lookup predictive event or causesverification of a lookup predictive event in accordance with parametersspecified in the applicable lookup event summary record and/or dataderived therefrom. In response to detecting a lookup predictive event, alookup operation is performed in advance of actual demand for the lookedup data, and the looked up data provided to a buffer or cache accessibleby the processing element which is predicted to require it. The lookupoperation may be delayed beyond the first indication of a lookuppredictive event if delay data indicates that delay is feasible withoutcausing the processing element which is predicted to require the lookedup data to wait for the data.

In one or more embodiments, upon initiation of a data streamingapplication and/or from time to time thereafter, stream manager 134accesses lookup event summary records 526 to generate lookup mask 521and one or more trigger parameter datasets 522 in management system 105.Local copies of the lookup mask are lookup and trigger parameterdatasets are then transmitted to the various compute nodes 110. Thelocal copies of these data structures are intended to be state datastructures which are maintained in compute node memory 300 and governthe execution of the various processing elements 311 in compute nodes110.

FIG. 13 is a flow diagram illustrating at a high level a process ofgenerating or updating state data structures which govern lookupoperations during execution, according to various embodiments. Referringto FIG. 13, stream manager 134 detects a condition forinitializing/updating state data which governs anticipatory lookupoperations during execution of the data streaming application (block1301). The condition might be an initialization of the data streamingapplication. Alternatively, the state data may be updated from time totime to account for changes to system configuration, workload changes,for performance tuning, and so forth. This could be done according to afixed schedule, and/or when certain events which may indicate a need forit are detected, such as an alteration of system configuration. Thestream manager would not necessarily detect such a condition itself, butmay simply receive an external command to reset or update the lookupstate data.

Upon detection of the appropriate condition, stream manager accessesoperator graph 132 to initialize lookup mask 521 (block 1302). Thelookup mask is initialized with all processing elements or otherentities disabled. In one or more embodiments and as described herein,both the lookup mask and the lookup event summary records have agranularity of a processing element, i.e., a separate mask bit existsfor each processing element, and a separate lookup event summary recordexists for one or more processing elements. However, it will beunderstood that the granularity of the mask and/or lookup event summaryrecords could be different. For example, there could be a separate maskbit for each operator within a processing element and/or separate lookupevent summary records for at least some of the operators

If any lookup event summary records 526 have not been selected (the ‘Y’branch from block 1303), the stream manager selects and retrieves a nextlookup event summary record (block 1304).

Stream manager 134 determines a predictive lookup probability thresholdto be used for deciding whether to perform an anticipatory lookup forthe current lookup event summary record (block 1305). A lookup operationshould be performed if the predicted probability of need for the dataexceeds the threshold. In one or more embodiments, the threshold couldvary depending on a number of dynamic factors, and therefore isdetermined at run time. These factors may include any or all of: (a) acurrent level of activity, and particularly I/O activity on the I/Ochannel to be used in the lookup, where a higher current level ofactivity indicates greater overhead cost of the anticipatory lookupoperation and therefore a higher threshold; (b) an amount of data to beretrieved by the lookup, which again relates to the overhead cost of thelookup, a greater amount of data indicating a higher threshold; (c) asize of buffer memory in the buffer to receive the looked up data, thesmaller buffer size indicating a higher threshold due to the greaterlikelihood of buffer contention; and (d) a length of time to perform thelookup operation, the longer lookup time indicating a greater cost forfailing to look up data which is actually needed, and hence a lowerthreshold. Other or additional factors could be used. It will beobserved that, due to these dynamic factors, the probability thresholdmay be different for different lookup operations, and may vary with timefor the same lookup operation. However, in one or more alternativeembodiments, a fixed probability threshold is used, making block 1305unnecessary.

The stream manager then determines whether the lookup probabilitythreshold is met for the selected lookup event summary record 1101(block 1306). In this case, the lookup probability for the record is theratio of the number of lookup events 1105 to the number of internaltuples 1104, representing an approximate probability that, given anoccurrence of an internal tuple of the corresponding internal tuple type1103 at trigger location 1102, a subsequent lookup operation will benecessary. If this ratio exceeds the lookup probability thresholdpreviously determined, the ‘Y’ branch is taken from block 1306, and acorresponding trigger parameter dataset is created (block 1308).

The trigger parameter dataset created at block 1308 is an abbreviatedversion of data in the lookup event summary record 1101, containing onlywhat is necessary to manage anticipatory lookup operations during runtime. Since the stream manager has already determined that a probabilitythreshold is met, it is unnecessary for the trigger parameter datasetcreated at block 1308 to contain probability data, key fields or keyfield specifics, external state variable Ids or external state variablespecifics. The existence of the dataset is an indication that thecorresponding anticipatory lookup operation should be performed. Thestream manager then continues to block 1310.

If, at block 1306, the lookup probability for the record does not exceedthe lookup probability threshold, the ‘N’ branch is taken from block1306. In this case, any key field specific 1110 or external statevariable specific 1115 in the lookup event summary record 1101 isexamined to determine whether the corresponding probability 1110 or 1115exceeds the threshold. Such a probability exceeding the lookupprobability threshold indicates that, although the occurrences of arandom tuple of the type specified in internal type field 1103 attrigger location 1102 does not indicate a sufficiently high probabilityof a subsequent lookup event, if the key field and/or external statevariable have corresponding value within the range specified in theapplicable key field specific 1110 or external state variable specific1115, then the probability of a subsequent lookup event does indeed meetthe threshold, and an anticipatory lookup operation should be performed.Accordingly, the ‘Y’ branch is taken from block 1307, and acorresponding trigger parameter dataset is created (block 1309).

In an embodiment, compound conditions may be evaluated at block 1306 ifno single key field or external state variable specific meets the lookupprobability threshold. For example, even if two separate conditions ofrespective different key fields or external state variables do notindividually meet the lookup probability threshold, an estimate oflookup probability for a logical AND of the two conditions can be madefrom individual probability data (e.g., counts of number of antecedentevents and number of subsequent lookups required), and compared to thelookup probability threshold.

The trigger parameter dataset created at block 1309 is similar to thatcreated at block 1308, but contains additional data to specify theapplicable key field and/or external state variable condition orconditions. As in the case of the trigger parameter dataset created atblock 1308, it is not necessary to specify the actual probabilities inthe trigger parameter dataset, only the condition(s) which meet thelookup probability threshold. There could be multiple conditions, whichcould be specified as multiple logical ORs, where each logical conditionmay comprise one or more logically ANDed conditions. The stream managerthen continues to block 1310.

At block 1310, the corresponding mask bit in lookup mask 521 is thenenabled for the location identified in trigger location field 1102 ofthe selected lookup event record 1101. This mask bit enables triggeringof the lookup operation during execution.

In one or more embodiments, the stream manager further uses delayparameters 1107 to determine whether a sufficient delay time intervalexists between the time that a tuple of the type identified in internaltuple field 1103 is encountered in the trigger location 1102 (theantecedent lookup predictive event) and the subsequent lookup operationfor the lookup operation to be performed (block 1311). I.e., assuming ananticipatory lookup operation is initiated immediately upon detectingthe tuple at the trigger location, will the lookup operation complete bythe time the tuple needs the lookup up data in the lookup locationidentified in field 1106. Since historical lookup times and streamingdelay times will vary, the delay parameters preferably providesufficient data to make a projection to some desired degree ofconfidence. For example, a mean and standard deviation of delay timesand lookup times might be provided, so that the delay time interval isconsidered “sufficient” if some percentage of the lookup operations(e.g. 90%) complete on time. If the delay time is considered sufficient,the trigger parameter record is so marked. This determination is used toprune certain premature trigger parameter datasets has explained withrespect to blocks 1312-1319 below. The stream manager the returns toblock 1303 to select a next record.

If, at block 1307, none of the key field specifics or external statevariable specifics (or combinations thereof) indicates a lookupprobability in excess of the lookup probability threshold, the ‘N’branch is taken from block 1307. In this case, the lookup mask isunaltered (i.e., remains disabled for the corresponding triggerlocation), and no corresponding trigger parameter dataset is created. Asa result, during execution, no lookup will be performed from the currenttrigger location 1102. The stream manager then returns to block 1303 toconsider the next lookup event record.

When all lookup event records have been thus examined and processed, the‘N’ branch is taken from block 1303. The stream manager then prunescertain redundant or premature trigger parameter datasets and disablesthe corresponding mask bits, shown as blocks 1312-1319.

Pruning is performed to improve execution efficiency by reducingtriggering of lookup operations. Where a given type of internal tuplepasses through multiple processing elements before requiring data to belooked up, it is possible that more than one of these processingelements, and perhaps all of them, have corresponding lookup eventrecords which meet the lookup probability threshold, and consequentlycorresponding bits in the trigger mast are enabled and trigger parameterdatasets created. Absent pruning, the result would be to triggeranticipatory lookup at each of the multiple processing elements duringexecution (although other mechanisms, such as I/O operation queues, mayavoid duplicate operations being performed). This has two undesirableeffects. First, in some cases, a lookup operation may be triggeredearlier than necessary in a first processing element, when waiting untilthe tuple reaches a second processing element downstream of the firstmight reduce the number of lookup operations (due to changes/deletionsof tuples) and/or make the looked up data more current. Second, a lookupmay be triggered multiple times in different processing elements for thesame data.

The stream manager reviews the trigger parameter datasets for possiblepruning. This could be done in any order, although some orders (e.g.traversing the operator graph backwards) may be more efficient. If thereare any more trigger parameter dataset which have not been selected forpruning analysis (the ‘Y’ branch from block 1312), the stream managerselects a next dataset (block 1313).

The immediate successor(s) processing element of the selected dataset inthe operator graph is/are identified (block 1314). The “immediatesuccessor(s)” is/are the processing element(s) which next receive theinternal tuple of type identified in the selected dataset. Often, therewill be only one immediate successor, although there could be multiplesuccessors, indicating a branch in the operator graph data flow. If, forall immediate successors, the delay time interval between the occurrenceof the lookup predictive tuple in the corresponding processing elementand the subsequent lookup event is greater than the time required toperform the lookup (as determined previously at block 1311), or there isno corresponding trigger parameter dataset (indicating low probabilityof lookup for a particular path), then the ‘Y’ branch is taken fromblock 1315, and the currently selected trigger parameter dataset ispruned and the corresponding mask bit in trigger mask 521 is disabled(block 1316). Pruning is appropriate because the lookup operation canwait to be performed in a successor processing element. After pruning,stream manager returns to block 1312 to select a next trigger parameterdataset.

If, at block 1315, an immediate successor does not have sufficient delaytime interval to perform the lookup operation, then the ‘N’ branch istaken. In this case, the currently selected dataset is not prunedbecause the lookup should be performed at least as early as theprocessing element corresponding to the currently selected dataset. Ifan immediate successor's trigger parameter dataset does not containconditions which are included in the conditions of the currentlyselected trigger parameter dataset (i.e., contains a new condition whichis not necessarily triggered by the current trigger parameter dataset),the ‘N’ branch is taken from block 1317, and a next trigger parameterdataset is selected at block 1312. If an immediate successor's triggerparameter dataset contains conditions which are included in theconditions of the currently selected trigger parameter dataset, then ‘Y’branch is taken from block 1317, and the successor's trigger parameterdataset is pruned and the corresponding mask bit in trigger mask 521 isdisabled (block 1318). The reason for doing so is that any lookupperformed by the successor will be duplicative of the lookup performedby the currently selected trigger parameter dataset. All downstreamsuccessors of the pruned immediate successor are identified and, if theysimilarly contain conditions which are included in the conditions of thecurrently selected trigger parameter dataset, they are similarly prunedand the corresponding mask bit in trigger mask 521 is disabled as well(block 1319). The stream manager then returns to block 1312 to select anext trigger parameter dataset.

When all trigger parameter datasets have been thus reviewed and prunedas necessary, the ‘N’ branch is taken from block 1312. The streammanager then transmits local copies of the trigger mask 521 and triggerparameter datasets 522 to each compute node (block 1320, where they arestored as local trigger mask 321 and local trigger parameters datasets322, respectively. The local copies containing only the maskbits/datasets needed by the receiving compute node. The process ofgenerating/updating state data structures governing lookup operations atrun time then ends.

After initialization, the data streaming application executes in eachprocessing element as data (in the form of tuples) arrives in theprocessing element, and uses the local trigger mask 321 and localtrigger parameter datasets 322 to identify appropriate conditions fortriggering anticipatory lookup operations. FIG. 14 is a flow diagramillustrating an execution process of an exemplary processing element 311of the data streaming application, in which the processing element mayin appropriate cases trigger an anticipatory lookup operation using thelocal trigger mask 321 and local trigger parameter datasets, accordingto various embodiments.

Referring to FIG. 14, a tuple traversing the operator graph arrives inthe exemplary processing element for processing (block 1401).Anticipatory lookup instructions 616 in the common run-time code 614which are executed upon entry to the processing element check the localtrigger mask 321 (block 1402). Although this check is illustrated inFIG. 14 and described herein as being performed upon entry to theprocessing element, it will be understood that it could alternatively beperformed on exit or at any location in the processing element's codewhich will always execute.

If the corresponding trigger bit is set, the ‘Y’ branch it taken fromblock 1402, and lookup instructions 616 access the local triggerparameter dataset(s) 322 to find any datasets for which the triggerlocation is the current processing element (block 1403). It is possiblethat there could be more than one such dataset, i.e., more than onelookup operation triggered from the same processing element. The triggerparameter dataset specifies the condition or conditions of triggering.If any of the conditions is met, the ‘Y’ branch is taken from block1404, and the lookup instructions 616 use the lookup descriptor data inthe trigger parameter dataset and/or additional data available (e.g.,from the tuple) to initiate a lookup operation (block 1405). This maybe, e.g., by addressing an address in storage defined by the lookupdescriptor, by transmitting a request over a network to an entitydefined by the lookup descriptor for data defined by the lookupdescriptor, or otherwise. If none of the conditions is met, the ‘N’branch is taken from block 1404 and block 1405 is by-passed.

The processing element then continues to execute on the tuple. In thisexemplary embodiment, the processing element calls operators A, B and C,illustrated as blocks 1406-1408. Operators could be executedsequentially or in parallel, and the number of operators may vary. Whenall operators have finished executing, processing of the tuple iscomplete.

In the various embodiments described above, it is possible that the samelookup operation will be performed multiple times for the same databefore the actual need arises. Although an attempt to prune redundanttrigger parameter datasets which may cause redundant lookup operationsis described with respect to FIG. 13, pruning can not always guaranteethat there will be no redundant lookups. Other mechanisms beyond thescope of the present disclosure may reduce the number of redundantlookup operations. For example, an operating system may maintain arecord of storage access operations in progress, from which it canprevent redundant accesses to the same storage location. In the worstcase, there may be some redundant lookup operations causing a smalladditional utilization of storage channels, network bandwidth, or thelike.

Alternative Lookup Predictive Events

In various embodiments described above, a lookup predictive event is theoccurrence of a tuple of specified type in a specified location in theoperator graph. In some cases, the occurrence of the tuple alone is asufficient condition to trigger a lookup operation. In others, inaddition to the occurrence of the tuple, one or more tuple attributesand/or external state variables must satisfy specified conditions totrigger the lookup operation.

However, in one or more alternative embodiments, the lookup operationneed not be triggered by a particular tuple type at a particularoperator graph location, but by other conditions which might be detectedduring execution. For example, prediction of need for data may be basedat least in part on historical data retrieval patterns of the datastreaming application. Such historical patterns could include any or allof (a) time of day/week a data element is typically retrieved; (b) timeafter occurrence of a particular event; (c) existence of certainconditions; or (d) correlation with other data retrievals. Any or all ofthese historical data patterns might be detected by analysis of profiledata, and appropriate data structures and/or triggering mechanisms couldbe used to trigger the lookup operations responsive to the antecedentlookup predictive event.

Other Variations

Although a series of steps has been described above as one or morepreferred and/or alternate embodiments, it will be appreciated that manyvariations of a technique for reducing delays in a data streamingapplication caused by lookup operations are possible. In particular,some steps may be performed in a different order, different datastructures may be used, and/or different hardware or software resourcesmay be employed to perform functions described herein. Furthermore,although certain formulae, thresholds, logical conditions, and so forthmay have been disclosed as one or more embodiments, it will beappreciated that these formulae, thresholds, logical conditions, etc.,and variations thereof are only some of the possible embodiments, andthat other techniques could alternatively be used.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing.Examples of a computer readable storage medium are illustrated in FIG. 2as system memory 202 and data storage devices 225-227. A computerreadable storage medium, as used herein, is not to be construed as beingtransitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Unless inconsistent with the invention or otherwise qualified herein,computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Although a specific embodiment of the invention has been disclosed alongwith certain alternatives, it will be recognized by those skilled in theart that additional variations in form and detail may be made within thescope of the following claims:

What is claimed is:
 1. A computer-executed method, comprising: usinglookup predictive profiling data to identify at least one lookuppredictive event occurring during execution of a data streamingapplication, each lookup predictive event predicting a respective lookupoperation to be performed in the future, the respective lookup operationretrieving respective data responsive to a determination by a respectiveprocessing element of said data streaming application of a need for therespective data; responsive to detecting, during a current executioninstance of said data streaming application, the occurrence of a lookuppredictive event identified by said using lookup predictive profilingdata to identify at least one lookup predictive event, initiatingretrieval, during the current execution instance of said data streamingapplication, of the respective data which is retrieved by the respectivelookup operation which the respective lookup predictive event predictswill be performed in the future, wherein said initiating retrieval ofthe respective data which is retrieved by the respective lookupoperation is performed before the respective processing elementdetermines the need for the respective data.
 2. The computer-executedmethod of claim 1, wherein said lookup predictive profiling datacomprises data obtained by collecting trace data from at least oneexecution instance of said data streaming application, and analyzing thecollected trace data to produce lookup predictive profiling data.
 3. Thecomputer-executed method of claim 1, wherein said lookup predictiveevent comprises a tuple of specified type detected at a specifiedlocation in an operator graph of said data streaming application.
 4. Thecomputer-executed method of claim 3, wherein said lookup predictiveevent further comprises an attribute value within a specified range, theattribute being an attribute of the tuple of specified type detected atthe specified location in the operation graph.
 5. The computer-executedmethod of claim 3, wherein said lookup predictive event furthercomprises a value of at least one external state variable.
 6. Thecomputer-executed method of claim 1, further comprising: determining,for each of the at least one lookup predictive event occurring duringexecution of the data streaming application, whether a respective lookupoperation may be delayed after detection of a corresponding lookuppredictive event.
 7. A computer-executed method for managing a datastreaming application having a plurality of processing elements,comprising: accessing profiling data with respect to said data streamingapplication, the profiling data being collected from one or more timeintervals during execution of said data streaming application; using theprofiling data to identify a lookup predictive event occurring duringexecution of said data streaming application, the lookup predictiveevent predicting a lookup operation to be performed in the future, thelookup operation retrieving lookup data responsive to a determination bya first processing element of said data streaming application of a needfor the lookup data during a current execution instance of said datastreaming application; detecting occurrence of the lookup predictiveevent during the current execution instance of said data streamingapplication, the lookup predictive event being detected in a secondprocessing element of said data streaming application, the secondprocessing element being upstream of the first processing element;responsive to detecting occurrence of the lookup predictive event duringthe current execution instance of said data streaming application,initiating retrieval of the lookup data to the first processing element,wherein said initiating retrieval of the lookup data to the firstprocessing element is performed before the first processing elementdetermines the need for the lookup data.
 8. The computer-executed methodof claim 7, wherein said profiling data comprises data obtained bycollecting trace data from at least one execution instance of said datastreaming application, and analyzing the collected trace data to producesaid profiling data.
 9. The computer-executed method of claim 7, whereinsaid lookup predictive event comprises a tuple of specified typedetected at the second processing element.
 10. The computer-executedmethod of claim 9, wherein said lookup predictive event furthercomprises at least one of: (a) an attribute value within a specifiedrange, the attribute being an attribute of the tuple of specified typedetected at the second processing element, and (b) a value of at leastone external state variable.
 11. The computer-executed method of claim7, further comprising: determining, for each of the at least one lookuppredictive event occurring during execution of said data streamingapplication, whether a respective lookup operation may be delayed afterdetection of a corresponding lookup predictive event.
 12. Acomputer-executed method, comprising: executing a data streamingapplication, said data streaming application implementing an operatorgraph having a plurality of processing elements which operate on datatuples being transferred among processing elements of the plurality ofprocessing elements; accessing lookup predictive profiling data, thelookup predictive profiling data identifying at least one lookuppredictive event, each lookup predictive event comprising a respectiveoccurrence during processing of a respective tuple in a respectiveantecedent processing element implemented by execution of said datastreaming application, each lookup predictive event predicting arespective lookup operation to be performed in the future, therespective lookup operation retrieving respective lookup data responsiveto a determination of a need for the respective lookup data, thedetermination of the need for the respective lookup data being made by arespective subsequent processing element implemented by execution ofsaid data streaming application, the respective subsequent processingelement processing the respective tuple after the respective antecedentprocessing element processes the respective tuple; detecting occurrenceof at least one lookup predictive event during processing of arespective tuple in a respective antecedent processing elementidentified by the lookup predictive profiling data during execution ofsaid data streaming application; responsive to detecting occurrence ofat least one lookup predictive event during processing of the respectivetuple in the respective antecedent processing element, initiatingretrieval of the respective lookup data which is retrieved by therespective lookup operation which the respective lookup predictive eventpredicts will be performed in the future to the respective subsequentprocessing element, wherein said initiating retrieval of the respectivelookup data is performed before the respective tuple is processed by therespective subsequent processing element.
 13. The computer-executedmethod of claim 12, wherein said lookup predictive profiling datacomprises data obtained by collecting trace data from at least oneexecution instance of said data streaming application, and analyzing thecollected trace data to produce lookup predictive profiling data. 14.The computer-executed method of claim 12, wherein said lookup predictiveevent further comprises at least one of: (a) an attribute value within aspecified range, the attribute being an attribute of the respectivetuple in the respective antecedent processing element, and (b) a valueof at least one external state variable.
 15. The computer-executedmethod of claim 12, further comprising: determining, for each of the atleast one lookup predictive event occurring during execution of the datastreaming application, whether a respective lookup operation may bedelayed after detection of a corresponding lookup predictive event. 16.The computer-executed method of claim 12, wherein executing said datastreaming application comprises implementing the operator graph in aplurality of computer systems coupled to at least one network supportingcommunication of data among the plurality of computer systems, eachcomputer system having a respective at least one physical processor anda respective physical memory, each computer system implementing arespective at least one processing element of the plurality ofprocessing elements.
 17. The computer-executed method of claim 16,wherein executing said data streaming application further comprisesexecuting a stream manager for said data streaming application in atleast one of the plurality of computer systems, the stream managerdetecting occurrence of the at least one lookup predictive eventidentified by the lookup predictive profiling data during execution ofsaid data streaming application.